## ----setup, include = FALSE-----------------------------------------
knitr::opts_chunk$set(
  prompt = TRUE,
  comment = NA,
  fig.path = "figures/",
  fig.width = 6,
  fig.height = 4.5
)
old_opt <- options(
  prompt = "R> ",
  continue = "+  ",
  width = 70,
  useFancyQuotes = FALSE
)
options(prompt = "R> ", continue = "+  ", width = 70, useFancyQuotes = FALSE)

library("spca")
source("Extended_vignette_material/spca_utilities_for_vignettes.R")

## ----load_all, include = FALSE--------------------------------------
load("Extended_vignette_material/spca_JSS_article_results.rda")

## ----load_ho--------------------------------------------------------
data("holzinger")
dim(holzinger)
data("holzinger_scales")

## ----pca, fig.cap = "Screeplot."------------------------------------
ho_pca = pca(holzinger, screeplot = TRUE, qq_plot = FALSE)

## ----qqplot, fig.cap = "qq-plot with a fitted line."----------------
wachter_qqplot(ho_pca$eigenvalues, p = ncol(holzinger),
               n = nrow(holzinger), n_fitline = -3)

## ----spcadef, eval = FALSE------------------------------------------
#    ho_spcadef = spca(M = holzinger,
#                    alpha = 0.95,          # 95% CVEXP
#                    n_comps = 4,           # 4 components
#                    method = "c",          # cSPCA
#                    var_selection = "f",   # forward
#                    objective = "cvexp",   # stop on CVEXP
#                    intensive = FALSE      # select by R-squared
#                  )

## ----print_spca-----------------------------------------------------
ho_spcadef

## ----summary--------------------------------------------------------
summary(ho_spcadef, cor_with_pc = TRUE)

## ----cor_comps------------------------------------------------------
round(ho_spcadef$spc_cor, 2)

## ----barplot, fig.cap = "Bar plots of the contributions of each sPC."----
plot(ho_spcadef)

## ----circplot, fig.cap = "Circular bar plots of the contributions of each sPC."----
plot(ho_spcadef, n_plot = 3, plot_type = "c", controls = list(color_scale = "printsafe"))

## ----heatmap, fig.cap = "Heat maps of the contributions of each sPC compared with the corresponding PC contributions."----
plot(ho_spcadef, pc_loadings = ho_pca$contributions, plot_type = "h")

## ----fixed, eval = FALSE--------------------------------------------
#  ho_spcafixed = spca(holzinger, alpha = 0.95, n_comps = 4,
#                   fixed_index_list = holzinger_scales)

## ----fix_load-------------------------------------------------------
ho_spcafixed

## ----fix_sum--------------------------------------------------------
summary(ho_spcafixed, cor_with_pc = TRUE)

## ----pspca, eval = FALSE--------------------------------------------
#  ho_pspca = spca(holzinger, n_comps = 4, alpha = 0.95,  method = "p",
#                  objective = "r2", var_selection = "b")

## ----compare, fig.cap = "Bar plots of the contributions of two different spca fits."----
compare_spca(list(ho_spcadef, ho_pspca), plot_loadings = TRUE,
             color_scale = "c",
             print_loadings = FALSE,
             col_short_names = TRUE,
             methods_names = c("cSPCA", "pSPCA")
             )

## ----aggr-----------------------------------------------------------
aggregate_by_group(ho_spcadef, groups = holzinger_scales)

## ----groupplot, fig.cap = "Bar plots of the contributions filled by groups."----
plot(ho_spcadef, variable_groups = holzinger_scales, controls =
       list( legend_position = "right")
)

## ----new_spca-------------------------------------------------------
A = cbind(ho_spcadef$loadings[, 1], ho_pspca$loadings[, 2])
ho_r = cor(holzinger)
ho_spcahyb = new_spca(A, ho_r, method_name = "hybrid")

## ----print_new_spca-------------------------------------------------
is.spca(ho_spcahyb)

## ----load_msc, include = FALSE--------------------------------------
    load("Extended_vignette_material/msc.rda", verbose = FALSE)
    load("Extended_vignette_material/ms_scalesh_fac.rda", verbose = FALSE)
     mss = scale(msc, center = FALSE, scale = TRUE)

## ----comp_methods, eval = FALSE-------------------------------------
#  met = c("uspca", "cspca", "pspca")
#  mss_met_spca = vector("list", 3)
#  
#  for(i in 1:3){
#    mss_met_spca[[i]] = spca(mss, n_comps = 4, method = met[i])
#  }
#  
#  mss_met_table = make_comparative_table(L = mss_met_spca, ind = 1:3,
#                                         pRAM = NULL, par_name = "method",
#                                         par_values = met)

## ----comp_meth, echo = FALSE----------------------------------------
mss_met_table

## ----comp_varsel, echo = FALSE--------------------------------------
mss_varsel_table

## ----comp_alpha_create, eval = FALSE--------------------------------
#  alpha = c(0.90, 0.95, 0.98)
#  
#  mss_alpha_spca = vector("list", 3)
#  
#  for(i in 1:3){
#    mss_alpha_spca[[i]] = spca(mss, alpha = alpha[i], n_comps = 4)
#  }
#  
#  mss_alpha_table = make_comparative_table(
#    L = mss_alpha_spca, pRAM = NULL,
#    par_name = "alpha", par_values = alpha
#  )

## ----comp_alpha, echo = FALSE---------------------------------------
mss_alpha_table

## ----conv_sum, echo = FALSE-----------------------------------------
compare_spca(list(mss_spcadef, mss_en_spcadef_obj),
             methods_names = c("ls", "el"),  col_short_names = FALSE,
             print_loadings = FALSE, plot_loadings = FALSE, print_tables = TRUE, return_tables = FALSE)

## ----mss_aggr, echo = FALSE-----------------------------------------
print(mss_agg_by_scale_print, quote = FALSE)

## ----gas_print, echo = FALSE----------------------------------------
compare_spca(list(gas_lsspca, gas_enspca_obj, gas_abspca_obj),
             x_axis_var_names = FALSE,
             methods_names = c("ls", "en", "ab"),
             col_short_names = FALSE,
             plot_loadings = FALSE, print_loadings = FALSE)

## ----cleanup, include = FALSE-------------------------------------------------
options(old_opt)

